blackUpsampling L-Edges.
We promote the learning-based edges – CATS [1]}, rather than canny edges in [2]}. Such priors can also be well generalized to the cases of higher resolution with the newly introduced E-NMS and our SSU trained with lines. As in Fig. REF , we take E-NMS in edge evaluation of [3]} and implement E-NMS in PyTorch to filter uncertain edge predictions. Then the filtered edges are further binarized. Thus SSU can iteratively upsample CATS edges as lines. E-NMS significantly eliminates ambiguous blur and artifacts near the boundaries, and improves the performance during HR inpainting as in Sec. REF . Potentially to avoid losing information, we maintain low confident areas with resized CATS prediction, while high confident boundaries are processed as E-NMS boundaries. More details about the E-NMS are discussed in the supplementary.
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